Overview

Dataset statistics

Number of variables24
Number of observations1001
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory187.8 KiB
Average record size in memory192.1 B

Variable types

Numeric21
Categorical3

Alerts

BILL_AMT1 is highly overall correlated with BILL_AMT2 and 11 other fieldsHigh correlation
BILL_AMT2 is highly overall correlated with BILL_AMT1 and 11 other fieldsHigh correlation
BILL_AMT3 is highly overall correlated with BILL_AMT1 and 15 other fieldsHigh correlation
BILL_AMT4 is highly overall correlated with BILL_AMT1 and 13 other fieldsHigh correlation
BILL_AMT5 is highly overall correlated with BILL_AMT1 and 13 other fieldsHigh correlation
BILL_AMT6 is highly overall correlated with BILL_AMT1 and 13 other fieldsHigh correlation
PAY_0 is highly overall correlated with PAY_2High correlation
PAY_2 is highly overall correlated with BILL_AMT1 and 9 other fieldsHigh correlation
PAY_3 is highly overall correlated with BILL_AMT1 and 9 other fieldsHigh correlation
PAY_4 is highly overall correlated with BILL_AMT1 and 9 other fieldsHigh correlation
PAY_5 is highly overall correlated with BILL_AMT1 and 9 other fieldsHigh correlation
PAY_6 is highly overall correlated with BILL_AMT1 and 9 other fieldsHigh correlation
PAY_AMT1 is highly overall correlated with BILL_AMT1 and 3 other fieldsHigh correlation
PAY_AMT2 is highly overall correlated with BILL_AMT3 and 6 other fieldsHigh correlation
PAY_AMT3 is highly overall correlated with BILL_AMT1 and 10 other fieldsHigh correlation
PAY_AMT4 is highly overall correlated with BILL_AMT3 and 7 other fieldsHigh correlation
PAY_AMT5 is highly overall correlated with BILL_AMT3 and 5 other fieldsHigh correlation
PAY_AMT6 is highly overall correlated with BILL_AMT3 and 6 other fieldsHigh correlation
PAY_0 has 473 (47.3%) zerosZeros
PAY_2 has 525 (52.4%) zerosZeros
PAY_3 has 512 (51.1%) zerosZeros
PAY_4 has 538 (53.7%) zerosZeros
PAY_5 has 542 (54.1%) zerosZeros
PAY_6 has 499 (49.9%) zerosZeros
BILL_AMT1 has 75 (7.5%) zerosZeros
BILL_AMT2 has 100 (10.0%) zerosZeros
BILL_AMT3 has 113 (11.3%) zerosZeros
BILL_AMT4 has 131 (13.1%) zerosZeros
BILL_AMT5 has 138 (13.8%) zerosZeros
BILL_AMT6 has 155 (15.5%) zerosZeros
PAY_AMT1 has 183 (18.3%) zerosZeros
PAY_AMT2 has 205 (20.5%) zerosZeros
PAY_AMT3 has 225 (22.5%) zerosZeros
PAY_AMT4 has 233 (23.3%) zerosZeros
PAY_AMT5 has 237 (23.7%) zerosZeros
PAY_AMT6 has 275 (27.5%) zerosZeros

Reproduction

Analysis started2024-03-26 05:58:45.879457
Analysis finished2024-03-26 06:00:06.370355
Duration1 minute and 20.49 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

LIMIT_BAL
Real number (ℝ)

Distinct56
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167532.47
Minimum10000
Maximum700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-26T06:00:06.502156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile20000
Q150000
median140000
Q3240000
95-th percentile420000
Maximum700000
Range690000
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation130587.92
Coefficient of variation (CV)0.77947829
Kurtosis0.54433431
Mean167532.47
Median Absolute Deviation (MAD)90000
Skewness1.0110186
Sum1.677 × 108
Variance1.7053205 × 1010
MonotonicityNot monotonic
2024-03-26T06:00:06.713850image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 129
 
12.9%
20000 58
 
5.8%
30000 57
 
5.7%
200000 52
 
5.2%
80000 44
 
4.4%
180000 36
 
3.6%
360000 35
 
3.5%
100000 33
 
3.3%
140000 32
 
3.2%
60000 29
 
2.9%
Other values (46) 496
49.6%
ValueCountFrequency (%)
10000 13
 
1.3%
20000 58
5.8%
30000 57
5.7%
40000 10
 
1.0%
50000 129
12.9%
60000 29
 
2.9%
70000 23
 
2.3%
80000 44
 
4.4%
90000 25
 
2.5%
100000 33
 
3.3%
ValueCountFrequency (%)
700000 1
 
0.1%
630000 2
 
0.2%
620000 1
 
0.1%
610000 1
 
0.1%
600000 1
 
0.1%
580000 1
 
0.1%
510000 2
 
0.2%
500000 22
2.2%
490000 2
 
0.2%
480000 2
 
0.2%

SEX
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
2
590 
1
411 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 590
58.9%
1 411
41.1%

Length

2024-03-26T06:00:06.903889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-26T06:00:07.075347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2 590
58.9%
1 411
41.1%

Most occurring characters

ValueCountFrequency (%)
2 590
58.9%
1 411
41.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1001
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 590
58.9%
1 411
41.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1001
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 590
58.9%
1 411
41.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1001
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 590
58.9%
1 411
41.1%

EDUCATION
Real number (ℝ)

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7762238
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-26T06:00:07.189796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.75091553
Coefficient of variation (CV)0.42275953
Kurtosis1.7150332
Mean1.7762238
Median Absolute Deviation (MAD)1
Skewness0.8750189
Sum1778
Variance0.56387413
MonotonicityNot monotonic
2024-03-26T06:00:07.480889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 447
44.7%
1 396
39.6%
3 151
 
15.1%
5 3
 
0.3%
4 2
 
0.2%
6 2
 
0.2%
ValueCountFrequency (%)
1 396
39.6%
2 447
44.7%
3 151
 
15.1%
4 2
 
0.2%
5 3
 
0.3%
6 2
 
0.2%
ValueCountFrequency (%)
6 2
 
0.2%
5 3
 
0.3%
4 2
 
0.2%
3 151
 
15.1%
2 447
44.7%
1 396
39.6%

MARRIAGE
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
2
570 
1
409 
3
 
19
0
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1001
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 570
56.9%
1 409
40.9%
3 19
 
1.9%
0 3
 
0.3%

Length

2024-03-26T06:00:07.628493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-26T06:00:07.792908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2 570
56.9%
1 409
40.9%
3 19
 
1.9%
0 3
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2 570
56.9%
1 409
40.9%
3 19
 
1.9%
0 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1001
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 570
56.9%
1 409
40.9%
3 19
 
1.9%
0 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1001
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 570
56.9%
1 409
40.9%
3 19
 
1.9%
0 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1001
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 570
56.9%
1 409
40.9%
3 19
 
1.9%
0 3
 
0.3%

AGE
Real number (ℝ)

Distinct44
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.945055
Minimum21
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-26T06:00:07.954139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q128
median33
Q341
95-th percentile53
Maximum75
Range54
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.2197602
Coefficient of variation (CV)0.2638359
Kurtosis0.23723143
Mean34.945055
Median Absolute Deviation (MAD)6
Skewness0.81757011
Sum34980
Variance85.003978
MonotonicityNot monotonic
2024-03-26T06:00:08.164931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
29 57
 
5.7%
27 57
 
5.7%
28 48
 
4.8%
30 48
 
4.8%
34 47
 
4.7%
32 46
 
4.6%
24 43
 
4.3%
26 41
 
4.1%
31 39
 
3.9%
25 37
 
3.7%
Other values (34) 538
53.7%
ValueCountFrequency (%)
21 1
 
0.1%
22 27
2.7%
23 35
3.5%
24 43
4.3%
25 37
3.7%
26 41
4.1%
27 57
5.7%
28 48
4.8%
29 57
5.7%
30 48
4.8%
ValueCountFrequency (%)
75 1
 
0.1%
73 1
 
0.1%
63 1
 
0.1%
61 1
 
0.1%
60 3
 
0.3%
59 3
 
0.3%
58 6
0.6%
57 6
0.6%
56 10
1.0%
55 7
0.7%

PAY_0
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.004995005
Minimum-2
Maximum8
Zeros473
Zeros (%)47.3%
Negative294
Negative (%)29.4%
Memory size7.9 KiB
2024-03-26T06:00:08.340807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1734458
Coefficient of variation (CV)-234.92385
Kurtosis8.0809239
Mean-0.004995005
Median Absolute Deviation (MAD)1
Skewness1.5091582
Sum-5
Variance1.376975
MonotonicityNot monotonic
2024-03-26T06:00:08.484507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 473
47.3%
-1 214
21.4%
1 137
 
13.7%
2 83
 
8.3%
-2 80
 
8.0%
3 6
 
0.6%
4 4
 
0.4%
8 4
 
0.4%
ValueCountFrequency (%)
-2 80
 
8.0%
-1 214
21.4%
0 473
47.3%
1 137
 
13.7%
2 83
 
8.3%
3 6
 
0.6%
4 4
 
0.4%
8 4
 
0.4%
ValueCountFrequency (%)
8 4
 
0.4%
4 4
 
0.4%
3 6
 
0.6%
2 83
 
8.3%
1 137
 
13.7%
0 473
47.3%
-1 214
21.4%
-2 80
 
8.0%

PAY_2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.16183816
Minimum-2
Maximum7
Zeros525
Zeros (%)52.4%
Negative338
Negative (%)33.8%
Memory size7.9 KiB
2024-03-26T06:00:08.632707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum7
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.228732
Coefficient of variation (CV)-7.5923499
Kurtosis4.3306832
Mean-0.16183816
Median Absolute Deviation (MAD)0
Skewness1.2084102
Sum-162
Variance1.5097822
MonotonicityNot monotonic
2024-03-26T06:00:08.765676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 525
52.4%
-1 206
 
20.6%
-2 132
 
13.2%
2 123
 
12.3%
3 8
 
0.8%
7 4
 
0.4%
5 1
 
0.1%
4 1
 
0.1%
1 1
 
0.1%
ValueCountFrequency (%)
-2 132
 
13.2%
-1 206
 
20.6%
0 525
52.4%
1 1
 
0.1%
2 123
 
12.3%
3 8
 
0.8%
4 1
 
0.1%
5 1
 
0.1%
7 4
 
0.4%
ValueCountFrequency (%)
7 4
 
0.4%
5 1
 
0.1%
4 1
 
0.1%
3 8
 
0.8%
2 123
 
12.3%
1 1
 
0.1%
0 525
52.4%
-1 206
 
20.6%
-2 132
 
13.2%

PAY_3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.16483516
Minimum-2
Maximum7
Zeros512
Zeros (%)51.1%
Negative347
Negative (%)34.7%
Memory size7.9 KiB
2024-03-26T06:00:08.907650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum7
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2624588
Coefficient of variation (CV)-7.6589167
Kurtosis3.9520619
Mean-0.16483516
Median Absolute Deviation (MAD)0
Skewness1.2268513
Sum-165
Variance1.5938022
MonotonicityNot monotonic
2024-03-26T06:00:09.047161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 512
51.1%
-1 208
20.8%
-2 139
 
13.9%
2 129
 
12.9%
4 4
 
0.4%
6 4
 
0.4%
7 2
 
0.2%
3 1
 
0.1%
1 1
 
0.1%
5 1
 
0.1%
ValueCountFrequency (%)
-2 139
 
13.9%
-1 208
20.8%
0 512
51.1%
1 1
 
0.1%
2 129
 
12.9%
3 1
 
0.1%
4 4
 
0.4%
5 1
 
0.1%
6 4
 
0.4%
7 2
 
0.2%
ValueCountFrequency (%)
7 2
 
0.2%
6 4
 
0.4%
5 1
 
0.1%
4 4
 
0.4%
3 1
 
0.1%
2 129
 
12.9%
1 1
 
0.1%
0 512
51.1%
-1 208
20.8%
-2 139
 
13.9%

PAY_4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.28371628
Minimum-2
Maximum7
Zeros538
Zeros (%)53.7%
Negative362
Negative (%)36.2%
Memory size7.9 KiB
2024-03-26T06:00:09.190058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum7
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1846622
Coefficient of variation (CV)-4.1755172
Kurtosis4.4357101
Mean-0.28371628
Median Absolute Deviation (MAD)0
Skewness1.2170372
Sum-284
Variance1.4034246
MonotonicityNot monotonic
2024-03-26T06:00:09.325390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 538
53.7%
-1 204
 
20.4%
-2 158
 
15.8%
2 87
 
8.7%
3 5
 
0.5%
5 5
 
0.5%
4 2
 
0.2%
7 2
 
0.2%
ValueCountFrequency (%)
-2 158
 
15.8%
-1 204
 
20.4%
0 538
53.7%
2 87
 
8.7%
3 5
 
0.5%
4 2
 
0.2%
5 5
 
0.5%
7 2
 
0.2%
ValueCountFrequency (%)
7 2
 
0.2%
5 5
 
0.5%
4 2
 
0.2%
3 5
 
0.5%
2 87
 
8.7%
0 538
53.7%
-1 204
 
20.4%
-2 158
 
15.8%

PAY_5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.28371628
Minimum-2
Maximum7
Zeros542
Zeros (%)54.1%
Negative356
Negative (%)35.6%
Memory size7.9 KiB
2024-03-26T06:00:09.471714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum7
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1702242
Coefficient of variation (CV)-4.1246281
Kurtosis3.7404585
Mean-0.28371628
Median Absolute Deviation (MAD)0
Skewness1.0532178
Sum-284
Variance1.3694246
MonotonicityNot monotonic
2024-03-26T06:00:09.607059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 542
54.1%
-1 194
 
19.4%
-2 162
 
16.2%
2 90
 
9.0%
3 5
 
0.5%
4 5
 
0.5%
7 2
 
0.2%
5 1
 
0.1%
ValueCountFrequency (%)
-2 162
 
16.2%
-1 194
 
19.4%
0 542
54.1%
2 90
 
9.0%
3 5
 
0.5%
4 5
 
0.5%
5 1
 
0.1%
7 2
 
0.2%
ValueCountFrequency (%)
7 2
 
0.2%
5 1
 
0.1%
4 5
 
0.5%
3 5
 
0.5%
2 90
 
9.0%
0 542
54.1%
-1 194
 
19.4%
-2 162
 
16.2%

PAY_6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.31468531
Minimum-2
Maximum7
Zeros499
Zeros (%)49.9%
Negative391
Negative (%)39.1%
Memory size7.9 KiB
2024-03-26T06:00:09.747075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum7
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2032764
Coefficient of variation (CV)-3.823745
Kurtosis3.3288878
Mean-0.31468531
Median Absolute Deviation (MAD)1
Skewness1.0646201
Sum-315
Variance1.4478741
MonotonicityNot monotonic
2024-03-26T06:00:09.890565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 499
49.9%
-1 218
21.8%
-2 173
 
17.3%
2 98
 
9.8%
3 8
 
0.8%
6 3
 
0.3%
4 1
 
0.1%
7 1
 
0.1%
ValueCountFrequency (%)
-2 173
 
17.3%
-1 218
21.8%
0 499
49.9%
2 98
 
9.8%
3 8
 
0.8%
4 1
 
0.1%
6 3
 
0.3%
7 1
 
0.1%
ValueCountFrequency (%)
7 1
 
0.1%
6 3
 
0.3%
4 1
 
0.1%
3 8
 
0.8%
2 98
 
9.8%
0 499
49.9%
-1 218
21.8%
-2 173
 
17.3%

BILL_AMT1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct904
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49386.738
Minimum-14386
Maximum507726
Zeros75
Zeros (%)7.5%
Negative22
Negative (%)2.2%
Memory size7.9 KiB
2024-03-26T06:00:10.074957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-14386
5-th percentile0
Q13128
median21075
Q359901
95-th percentile199436
Maximum507726
Range522112
Interquartile range (IQR)56773

Descriptive statistics

Standard deviation72657.966
Coefficient of variation (CV)1.471204
Kurtosis8.9708107
Mean49386.738
Median Absolute Deviation (MAD)20679
Skewness2.6710274
Sum49436125
Variance5.2791801 × 109
MonotonicityNot monotonic
2024-03-26T06:00:10.298203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 75
 
7.5%
390 8
 
0.8%
780 4
 
0.4%
396 3
 
0.3%
316 3
 
0.3%
650 2
 
0.2%
550 2
 
0.2%
1261 2
 
0.2%
1440 2
 
0.2%
2000 2
 
0.2%
Other values (894) 898
89.7%
ValueCountFrequency (%)
-14386 1
0.1%
-2000 1
0.1%
-1312 1
0.1%
-1100 1
0.1%
-946 1
0.1%
-709 1
0.1%
-475 1
0.1%
-288 1
0.1%
-200 2
0.2%
-190 1
0.1%
ValueCountFrequency (%)
507726 1
0.1%
507062 1
0.1%
471814 1
0.1%
467150 1
0.1%
422069 1
0.1%
400134 1
0.1%
386405 1
0.1%
367965 1
0.1%
366193 1
0.1%
355215 1
0.1%

BILL_AMT2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct875
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47879.346
Minimum-13543
Maximum509229
Zeros100
Zeros (%)10.0%
Negative24
Negative (%)2.4%
Memory size7.9 KiB
2024-03-26T06:00:10.521970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-13543
5-th percentile0
Q13274
median20400
Q358472
95-th percentile196143
Maximum509229
Range522772
Interquartile range (IQR)55198

Descriptive statistics

Standard deviation72090.718
Coefficient of variation (CV)1.5056747
Kurtosis9.6830793
Mean47879.346
Median Absolute Deviation (MAD)20084
Skewness2.7771264
Sum47927225
Variance5.1970716 × 109
MonotonicityNot monotonic
2024-03-26T06:00:10.744127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 100
 
10.0%
390 5
 
0.5%
300 4
 
0.4%
780 4
 
0.4%
316 4
 
0.4%
396 3
 
0.3%
1261 3
 
0.3%
291 3
 
0.3%
-200 3
 
0.3%
1648 2
 
0.2%
Other values (865) 870
86.9%
ValueCountFrequency (%)
-13543 1
0.1%
-9850 1
0.1%
-1100 1
0.1%
-1041 1
0.1%
-946 1
0.1%
-818 1
0.1%
-709 1
0.1%
-707 1
0.1%
-425 1
0.1%
-303 1
0.1%
ValueCountFrequency (%)
509229 1
0.1%
491956 1
0.1%
478380 1
0.1%
458862 1
0.1%
431342 1
0.1%
412023 1
0.1%
398857 1
0.1%
387910 1
0.1%
372700 1
0.1%
363325 1
0.1%

BILL_AMT3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct863
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44925.682
Minimum-9850
Maximum499936
Zeros113
Zeros (%)11.3%
Negative22
Negative (%)2.2%
Memory size7.9 KiB
2024-03-26T06:00:10.955653image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-9850
5-th percentile0
Q11940
median19292
Q354477
95-th percentile186292
Maximum499936
Range509786
Interquartile range (IQR)52537

Descriptive statistics

Standard deviation69545.948
Coefficient of variation (CV)1.5480221
Kurtosis10.630694
Mean44925.682
Median Absolute Deviation (MAD)18902
Skewness2.9014972
Sum44970608
Variance4.8366389 × 109
MonotonicityNot monotonic
2024-03-26T06:00:11.171516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 113
 
11.3%
390 8
 
0.8%
-2 3
 
0.3%
316 3
 
0.3%
396 3
 
0.3%
780 3
 
0.3%
29366 2
 
0.2%
664 2
 
0.2%
18122 2
 
0.2%
8441 2
 
0.2%
Other values (853) 860
85.9%
ValueCountFrequency (%)
-9850 1
0.1%
-2697 1
0.1%
-1690 1
0.1%
-946 1
0.1%
-709 1
0.1%
-684 1
0.1%
-527 1
0.1%
-387 1
0.1%
-288 1
0.1%
-281 1
0.1%
ValueCountFrequency (%)
499936 1
0.1%
479432 1
0.1%
469703 1
0.1%
445007 1
0.1%
430637 1
0.1%
404205 1
0.1%
395612 1
0.1%
375948 1
0.1%
375070 1
0.1%
373181 1
0.1%

BILL_AMT4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct846
Distinct (%)84.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40748.409
Minimum-3684
Maximum628699
Zeros131
Zeros (%)13.1%
Negative22
Negative (%)2.2%
Memory size7.9 KiB
2024-03-26T06:00:11.390408image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-3684
5-th percentile0
Q11423
median17710
Q348851
95-th percentile167163
Maximum628699
Range632383
Interquartile range (IQR)47428

Descriptive statistics

Standard deviation68206.93
Coefficient of variation (CV)1.6738551
Kurtosis17.864868
Mean40748.409
Median Absolute Deviation (MAD)17314
Skewness3.5782032
Sum40789157
Variance4.6521852 × 109
MonotonicityNot monotonic
2024-03-26T06:00:11.596273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 131
 
13.1%
390 7
 
0.7%
316 5
 
0.5%
300 3
 
0.3%
792 2
 
0.2%
-2 2
 
0.2%
362 2
 
0.2%
5400 2
 
0.2%
2303 2
 
0.2%
5818 2
 
0.2%
Other values (836) 843
84.2%
ValueCountFrequency (%)
-3684 1
0.1%
-2898 1
0.1%
-2618 1
0.1%
-946 1
0.1%
-923 1
0.1%
-828 1
0.1%
-810 1
0.1%
-387 1
0.1%
-288 1
0.1%
-281 1
0.1%
ValueCountFrequency (%)
628699 1
0.1%
542653 1
0.1%
505507 1
0.1%
487066 1
0.1%
479978 1
0.1%
447130 1
0.1%
386295 1
0.1%
376657 1
0.1%
360199 1
0.1%
354839 1
0.1%

BILL_AMT5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct833
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39078.666
Minimum-28335
Maximum484612
Zeros138
Zeros (%)13.8%
Negative26
Negative (%)2.6%
Memory size7.9 KiB
2024-03-26T06:00:11.811926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-28335
5-th percentile0
Q11206
median17580
Q346404
95-th percentile165725
Maximum484612
Range512947
Interquartile range (IQR)45198

Descriptive statistics

Standard deviation63108.239
Coefficient of variation (CV)1.6149026
Kurtosis12.846901
Mean39078.666
Median Absolute Deviation (MAD)17183
Skewness3.1071798
Sum39117745
Variance3.9826498 × 109
MonotonicityNot monotonic
2024-03-26T06:00:12.189863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 138
 
13.8%
390 8
 
0.8%
150 3
 
0.3%
2000 3
 
0.3%
396 3
 
0.3%
316 3
 
0.3%
19450 2
 
0.2%
792 2
 
0.2%
-10 2
 
0.2%
1980 2
 
0.2%
Other values (823) 835
83.4%
ValueCountFrequency (%)
-28335 1
0.1%
-5000 1
0.1%
-3272 1
0.1%
-1488 1
0.1%
-1005 1
0.1%
-946 1
0.1%
-783 1
0.1%
-679 1
0.1%
-527 1
0.1%
-420 1
0.1%
ValueCountFrequency (%)
484612 1
0.1%
483003 1
0.1%
471145 1
0.1%
440982 1
0.1%
369532 1
0.1%
356656 1
0.1%
356636 1
0.1%
356206 1
0.1%
335760 1
0.1%
315820 1
0.1%

BILL_AMT6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct821
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38012.012
Minimum-339603
Maximum473944
Zeros155
Zeros (%)15.5%
Negative18
Negative (%)1.8%
Memory size7.9 KiB
2024-03-26T06:00:12.419251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-339603
5-th percentile0
Q1830
median15846
Q346557
95-th percentile167964
Maximum473944
Range813547
Interquartile range (IQR)45727

Descriptive statistics

Standard deviation63074.415
Coefficient of variation (CV)1.6593285
Kurtosis12.168596
Mean38012.012
Median Absolute Deviation (MAD)15646
Skewness2.6366912
Sum38050024
Variance3.9783818 × 109
MonotonicityNot monotonic
2024-03-26T06:00:12.634315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 155
 
15.5%
390 8
 
0.8%
316 4
 
0.4%
150 4
 
0.4%
291 3
 
0.3%
780 3
 
0.3%
1320 3
 
0.3%
-2 2
 
0.2%
101299 2
 
0.2%
-200 2
 
0.2%
Other values (811) 815
81.4%
ValueCountFrequency (%)
-339603 1
0.1%
-3272 1
0.1%
-1884 1
0.1%
-946 1
0.1%
-780 1
0.1%
-304 1
0.1%
-281 1
0.1%
-246 1
0.1%
-200 2
0.2%
-189 1
0.1%
ValueCountFrequency (%)
473944 1
0.1%
469961 1
0.1%
434715 1
0.1%
419643 1
0.1%
367399 1
0.1%
364089 1
0.1%
352257 1
0.1%
330121 1
0.1%
309959 1
0.1%
305498 1
0.1%

PAY_AMT1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct522
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5382.3397
Minimum0
Maximum199646
Zeros183
Zeros (%)18.3%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-26T06:00:12.867597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11000
median2184
Q35090
95-th percentile20000
Maximum199646
Range199646
Interquartile range (IQR)4090

Descriptive statistics

Standard deviation12180.755
Coefficient of variation (CV)2.2630967
Kurtosis88.348075
Mean5382.3397
Median Absolute Deviation (MAD)1944
Skewness7.7498929
Sum5387722
Variance1.483708 × 108
MonotonicityNot monotonic
2024-03-26T06:00:13.086542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 183
 
18.3%
2000 39
 
3.9%
3000 32
 
3.2%
2500 20
 
2.0%
10000 19
 
1.9%
5000 17
 
1.7%
1000 16
 
1.6%
1500 13
 
1.3%
4000 11
 
1.1%
1800 9
 
0.9%
Other values (512) 642
64.1%
ValueCountFrequency (%)
0 183
18.3%
1 1
 
0.1%
39 2
 
0.2%
92 1
 
0.1%
100 1
 
0.1%
105 1
 
0.1%
131 1
 
0.1%
138 1
 
0.1%
157 1
 
0.1%
165 1
 
0.1%
ValueCountFrequency (%)
199646 1
0.1%
120093 1
0.1%
120041 1
0.1%
90000 1
0.1%
81690 1
0.1%
80000 2
0.2%
70010 1
0.1%
67650 1
0.1%
57087 1
0.1%
55000 1
0.1%

PAY_AMT2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct520
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5051.4006
Minimum0
Maximum285138
Zeros205
Zeros (%)20.5%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-26T06:00:13.311093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1390
median1710
Q34500
95-th percentile16025
Maximum285138
Range285138
Interquartile range (IQR)4110

Descriptive statistics

Standard deviation15626.153
Coefficient of variation (CV)3.0934298
Kurtosis151.07827
Mean5051.4006
Median Absolute Deviation (MAD)1710
Skewness10.752948
Sum5056452
Variance2.4417666 × 108
MonotonicityNot monotonic
2024-03-26T06:00:13.508523image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 205
 
20.5%
2000 29
 
2.9%
3000 27
 
2.7%
5000 27
 
2.7%
1500 26
 
2.6%
1000 24
 
2.4%
1600 12
 
1.2%
1400 10
 
1.0%
1200 10
 
1.0%
390 9
 
0.9%
Other values (510) 622
62.1%
ValueCountFrequency (%)
0 205
20.5%
1 1
 
0.1%
2 2
 
0.2%
3 1
 
0.1%
5 1
 
0.1%
7 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
12 1
 
0.1%
15 1
 
0.1%
ValueCountFrequency (%)
285138 1
0.1%
199982 1
0.1%
177671 1
0.1%
145000 1
0.1%
104279 1
0.1%
88678 1
0.1%
84440 1
0.1%
75720 1
0.1%
55693 1
0.1%
52110 1
0.1%

PAY_AMT3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct496
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4176.1499
Minimum0
Maximum133657
Zeros225
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-26T06:00:13.717240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1228
median1206
Q33720
95-th percentile14328
Maximum133657
Range133657
Interquartile range (IQR)3492

Descriptive statistics

Standard deviation10514.648
Coefficient of variation (CV)2.517785
Kurtosis59.690678
Mean4176.1499
Median Absolute Deviation (MAD)1206
Skewness6.7443772
Sum4180326
Variance1.1055781 × 108
MonotonicityNot monotonic
2024-03-26T06:00:13.944462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 225
 
22.5%
1000 50
 
5.0%
2000 38
 
3.8%
3000 35
 
3.5%
5000 22
 
2.2%
1500 12
 
1.2%
10000 10
 
1.0%
6000 10
 
1.0%
500 9
 
0.9%
1100 9
 
0.9%
Other values (486) 581
58.0%
ValueCountFrequency (%)
0 225
22.5%
3 1
 
0.1%
27 1
 
0.1%
28 1
 
0.1%
50 1
 
0.1%
54 1
 
0.1%
87 1
 
0.1%
91 1
 
0.1%
100 1
 
0.1%
116 1
 
0.1%
ValueCountFrequency (%)
133657 1
0.1%
130000 1
0.1%
89000 1
0.1%
80000 1
0.1%
75940 1
0.1%
74354 1
0.1%
68454 1
0.1%
65840 1
0.1%
62520 1
0.1%
61411 1
0.1%

PAY_AMT4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct482
Distinct (%)48.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4671.4885
Minimum0
Maximum188840
Zeros233
Zeros (%)23.3%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-26T06:00:14.175309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1148
median1398
Q34000
95-th percentile17000
Maximum188840
Range188840
Interquartile range (IQR)3852

Descriptive statistics

Standard deviation13269.944
Coefficient of variation (CV)2.8406243
Kurtosis70.527373
Mean4671.4885
Median Absolute Deviation (MAD)1398
Skewness7.4547752
Sum4676160
Variance1.7609141 × 108
MonotonicityNot monotonic
2024-03-26T06:00:14.388678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 233
23.3%
1000 43
 
4.3%
2000 35
 
3.5%
5000 24
 
2.4%
3000 24
 
2.4%
1500 18
 
1.8%
4000 16
 
1.6%
500 13
 
1.3%
2500 11
 
1.1%
390 8
 
0.8%
Other values (472) 576
57.5%
ValueCountFrequency (%)
0 233
23.3%
6 3
 
0.3%
7 1
 
0.1%
17 1
 
0.1%
25 1
 
0.1%
64 1
 
0.1%
69 1
 
0.1%
74 1
 
0.1%
92 1
 
0.1%
98 1
 
0.1%
ValueCountFrequency (%)
188840 1
0.1%
146900 1
0.1%
107591 1
0.1%
100000 2
0.2%
99669 1
0.1%
99000 1
0.1%
97441 1
0.1%
88348 1
0.1%
80552 1
0.1%
79377 1
0.1%

PAY_AMT5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct480
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5331.05
Minimum0
Maximum195599
Zeros237
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-26T06:00:14.610470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1189
median1306
Q33745
95-th percentile17000
Maximum195599
Range195599
Interquartile range (IQR)3556

Descriptive statistics

Standard deviation16812.537
Coefficient of variation (CV)3.1537009
Kurtosis58.238593
Mean5331.05
Median Absolute Deviation (MAD)1306
Skewness7.0346324
Sum5336381
Variance2.826614 × 108
MonotonicityNot monotonic
2024-03-26T06:00:14.829382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 237
23.7%
1000 41
 
4.1%
3000 35
 
3.5%
2000 32
 
3.2%
1500 24
 
2.4%
5000 19
 
1.9%
4000 12
 
1.2%
500 9
 
0.9%
1200 8
 
0.8%
3500 8
 
0.8%
Other values (470) 576
57.5%
ValueCountFrequency (%)
0 237
23.7%
12 1
 
0.1%
60 1
 
0.1%
91 1
 
0.1%
100 1
 
0.1%
150 4
 
0.4%
160 1
 
0.1%
162 1
 
0.1%
169 1
 
0.1%
175 1
 
0.1%
ValueCountFrequency (%)
195599 1
 
0.1%
184922 1
 
0.1%
162000 1
 
0.1%
160719 1
 
0.1%
133841 1
 
0.1%
132200 1
 
0.1%
130291 1
 
0.1%
101005 1
 
0.1%
100000 3
0.3%
85900 1
 
0.1%

PAY_AMT6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct436
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5090.7043
Minimum0
Maximum528666
Zeros275
Zeros (%)27.5%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-03-26T06:00:15.049862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1250
Q33784
95-th percentile13770
Maximum528666
Range528666
Interquartile range (IQR)3784

Descriptive statistics

Standard deviation23658.888
Coefficient of variation (CV)4.6474685
Kurtosis289.91556
Mean5090.7043
Median Absolute Deviation (MAD)1250
Skewness15.241538
Sum5095795
Variance5.5974298 × 108
MonotonicityNot monotonic
2024-03-26T06:00:15.416100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 275
27.5%
2000 50
 
5.0%
1000 49
 
4.9%
3000 28
 
2.8%
5000 25
 
2.5%
1500 15
 
1.5%
2500 15
 
1.5%
4000 12
 
1.2%
10000 12
 
1.2%
6000 9
 
0.9%
Other values (426) 511
51.0%
ValueCountFrequency (%)
0 275
27.5%
1 1
 
0.1%
3 1
 
0.1%
4 1
 
0.1%
60 1
 
0.1%
62 1
 
0.1%
66 2
 
0.2%
95 1
 
0.1%
100 2
 
0.2%
102 2
 
0.2%
ValueCountFrequency (%)
528666 1
0.1%
345293 1
0.1%
185652 1
0.1%
167000 1
0.1%
153504 1
0.1%
126685 1
0.1%
105700 1
0.1%
77195 1
0.1%
68978 1
0.1%
67619 1
0.1%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
787 
1
214 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1001
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 787
78.6%
1 214
 
21.4%

Length

2024-03-26T06:00:15.595737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-26T06:00:15.752042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 787
78.6%
1 214
 
21.4%

Most occurring characters

ValueCountFrequency (%)
0 787
78.6%
1 214
 
21.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1001
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 787
78.6%
1 214
 
21.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1001
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 787
78.6%
1 214
 
21.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1001
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 787
78.6%
1 214
 
21.4%

Interactions

2024-03-26T06:00:01.748768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:46.914978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:50.801474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:54.385355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:58.155376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:01.888826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:05.373931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:08.999239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:12.586943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:16.253805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:19.739053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:23.549493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:27.156108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:30.933182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:34.665189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:38.484855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:42.372276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:46.398527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:50.198072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:54.032651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:57.923407image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T06:00:02.066976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:47.111685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:50.987525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:54.563862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:58.337363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:02.065084image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:05.548868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:09.175622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:12.764819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:16.434108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:19.931096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:23.727118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:27.348050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:31.116849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:34.842161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:38.681094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:42.563947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:46.585619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:50.391413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:54.219550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:58.115052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T06:00:02.231036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:47.290623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:51.155294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:54.886848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:58.506687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:02.224209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:05.709275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:09.339580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:12.925355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:16.596297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:20.099249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:23.895116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:27.527433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:31.289143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:35.010329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:38.862640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:42.914733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:46.752775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:50.567064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:54.407018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:58.291724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T06:00:02.405392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:47.471519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:51.317713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:55.046641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:58.670985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:02.393012image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:05.867854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:09.506036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:13.087616image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:16.759486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:20.268148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:24.059083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:27.697886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:31.465670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:35.184923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:39.041555image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:43.093430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:46.922152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:50.739707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:54.580609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:58.473772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T06:00:02.569412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:47.651148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:51.497389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:55.212359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:58.832574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:02.556573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:06.027576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:09.661223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:13.244423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:16.917232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:20.440892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:24.224842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:27.867945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:31.632938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:35.352871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:39.221496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:43.268541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:47.088935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:50.912285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:54.754858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:58.648068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T06:00:02.726283image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:47.817362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:51.653344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:55.370298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:58.985936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:02.710521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:06.176990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:09.810590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:13.402224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:17.069854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:20.608548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:24.387265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:28.027241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:31.799499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:35.518817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:39.398772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:43.445118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:47.253971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:51.076708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:54.919655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:58.821008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T06:00:02.881843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:47.984915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:51.806544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:55.532334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:59.140567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:02.861320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:06.325558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:09.966004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:13.556995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:17.220079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:20.767591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:24.547330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:28.189757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:31.962747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:35.688286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:39.573782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:43.614388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:47.419369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:51.246617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:55.087541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:58.989349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T06:00:03.039419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:48.151731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:51.963466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:55.686789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:59.296113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:03.014196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:06.477133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:10.121856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:13.706428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:17.374885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:20.925879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:24.704466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:28.348120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:32.130466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:35.844119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:39.744662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:43.786734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:47.582196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:51.421837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:55.253759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2024-03-26T05:59:30.219764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:33.904671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:37.748870image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:41.581678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:45.630701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:49.482558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:53.249959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:57.178170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T06:00:00.992550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T06:00:04.916500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:50.231071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:53.832453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:57.607128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:01.365243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:04.859432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:08.485257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:12.032394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:15.733490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:19.216877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:22.996650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:26.616368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:30.402182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:34.097505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:37.931360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:41.777920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:45.825696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:49.662592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:53.461174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:57.364034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T06:00:01.185616image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T06:00:05.097348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:50.432596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:54.024060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:57.788835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:01.541901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:05.035138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:08.659566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:12.206714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:15.908136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:19.393106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:23.176456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:26.800309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:30.581113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:34.295272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:38.111698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:41.984448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:46.019190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:49.841997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:53.652551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:57.549005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T06:00:01.377815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T06:00:05.283172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:50.631492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:54.209989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:58:57.986367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:01.726277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:05.213461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:08.840457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:12.411250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:16.092513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:19.578815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:23.383673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:26.988118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:30.768978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:34.490699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:38.301504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:42.184119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:46.219249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:50.026746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:53.849180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T05:59:57.747809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-26T06:00:01.572372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2024-03-26T06:00:15.920258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
AGEBILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6EDUCATIONLIMIT_BALMARRIAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6SEXdefault payment next month
AGE1.000-0.009-0.0150.001-0.0060.0100.0150.1590.2330.304-0.063-0.087-0.097-0.071-0.055-0.0620.0390.0970.0320.0230.0580.0510.1190.073
BILL_AMT1-0.0091.0000.9050.8610.8120.7660.7550.1500.0580.0000.2640.5860.5400.5450.5100.5100.5140.4810.5110.4610.4400.4610.0630.000
BILL_AMT2-0.0150.9051.0000.8920.8410.7890.7810.1390.0770.0000.2850.5670.5980.5690.5280.5350.6550.4820.5140.4740.4640.4840.0000.000
BILL_AMT30.0010.8610.8921.0000.9090.8600.8300.1290.0720.0000.2770.5430.5790.6640.6060.6020.5280.6330.5510.5280.5010.5170.0000.000
BILL_AMT4-0.0060.8120.8410.9091.0000.9010.8530.1320.0670.0000.2790.5490.5730.6650.6740.6450.4830.5630.6620.5300.4960.5370.0660.068
BILL_AMT50.0100.7660.7890.8600.9011.0000.8890.1360.0690.0000.2730.5050.5480.6280.6360.6950.4590.5430.5700.6750.4930.5750.0720.054
BILL_AMT60.0150.7550.7810.8300.8530.8891.0000.1170.0890.0000.2790.4830.5150.5930.5820.6550.4570.5350.5540.6020.6750.5820.0310.090
EDUCATION0.1590.1500.1390.1290.1320.1360.1171.000-0.2340.1180.1120.2260.2080.2110.1770.195-0.0130.001-0.0280.023-0.0180.0240.0730.000
LIMIT_BAL0.2330.0580.0770.0720.0670.0690.089-0.2341.0000.063-0.187-0.286-0.294-0.282-0.280-0.2840.2890.2420.2630.2330.3020.2930.1070.000
MARRIAGE0.3040.0000.0000.0000.0000.0000.0000.1180.0631.0000.0470.0450.0560.0630.0500.054-0.021-0.036-0.047-0.023-0.062-0.0420.0530.000
PAY_0-0.0630.2640.2850.2770.2790.2730.2790.112-0.1870.0471.0000.5260.4720.4250.3960.401-0.101-0.059-0.045-0.019-0.019-0.0090.0000.377
PAY_2-0.0870.5860.5670.5430.5490.5050.4830.226-0.2860.0450.5261.0000.7990.7090.6870.6490.0610.1270.1780.1250.1130.1580.0000.256
PAY_3-0.0970.5400.5980.5790.5730.5480.5150.208-0.2940.0560.4720.7991.0000.7900.7210.6800.2500.0710.1750.1740.1590.1780.0000.253
PAY_4-0.0710.5450.5690.6640.6650.6280.5930.211-0.2820.0630.4250.7090.7901.0000.8370.7700.1770.2880.1600.2140.1900.2350.0000.221
PAY_5-0.0550.5100.5280.6060.6740.6360.5820.177-0.2800.0500.3960.6870.7210.8371.0000.8180.1440.2560.3210.1580.1990.2500.0570.234
PAY_6-0.0620.5100.5350.6020.6450.6950.6550.195-0.2840.0540.4010.6490.6800.7700.8181.0000.1580.2550.2750.3450.1650.2780.0420.140
PAY_AMT10.0390.5140.6550.5280.4830.4590.457-0.0130.289-0.021-0.1010.0610.2500.1770.1440.1581.0000.4610.5160.4740.4850.4580.0000.035
PAY_AMT20.0970.4810.4820.6330.5630.5430.5350.0010.242-0.036-0.0590.1270.0710.2880.2560.2550.4611.0000.5420.5820.5240.4980.0890.095
PAY_AMT30.0320.5110.5140.5510.6620.5700.554-0.0280.263-0.047-0.0450.1780.1750.1600.3210.2750.5160.5421.0000.5380.5420.5570.0360.000
PAY_AMT40.0230.4610.4740.5280.5300.6750.6020.0230.233-0.023-0.0190.1250.1740.2140.1580.3450.4740.5820.5381.0000.5240.5750.0000.000
PAY_AMT50.0580.4400.4640.5010.4960.4930.675-0.0180.302-0.062-0.0190.1130.1590.1900.1990.1650.4850.5240.5420.5241.0000.5540.0940.034
PAY_AMT60.0510.4610.4840.5170.5370.5750.5820.0240.293-0.042-0.0090.1580.1780.2350.2500.2780.4580.4980.5570.5750.5541.0000.0150.046
SEX0.1190.0630.0000.0000.0660.0720.0310.0730.1070.0530.0000.0000.0000.0000.0570.0420.0000.0890.0360.0000.0940.0151.0000.036
default payment next month0.0730.0000.0000.0000.0680.0540.0900.0000.0000.0000.3770.2560.2530.2210.2340.1400.0350.0950.0000.0000.0340.0460.0361.000

Missing values

2024-03-26T06:00:05.582936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-26T06:00:06.148769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6default payment next month
05000012157-10-100086175670358352094019146191312000366811000090006896790
1500001123700000064400570695760819394196192002425001815657100010008000
2500000112290000003679654120234450075426534830034739445500040000380002023913750137700
3100000222230-1-100-111876380601221-1595673806010581168715420
41400002312800200011285140961210812211117933719332904321000100010000
52000013235-2-2-2-2-1-10000130071391200013007112200
62000002323400200-11107397875535251318283731230612503003738660
726000021251-1-1-1-1-121226121670996685172228713668218189966858322301036400
863000022241-10-1-1-1-112137650065006500650028701000650065006500287000
970000122301220026580267369657016678236137368943200030003000150001
LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1BILL_AMT2BILL_AMT3BILL_AMT4BILL_AMT5BILL_AMT6PAY_AMT1PAY_AMT2PAY_AMT3PAY_AMT4PAY_AMT5PAY_AMT6default payment next month
99120000011239-2-2-2-2-2-2-200-200-20006080000020060800000
992140000111450000223971640799418534445245433463831600160031691700170014950
993360000111381-2-2-2-2-20000000000001
9945000022223-1-1-10-1-1780078039039050007800390500183001
995120000122252200001133481101191117008385886434888020500031583934380220000
996100000121290000-1-1944539586067782-261895748101299332050000100000718600
9972000002212800000081865867908441970411035413632500020008900065009115040
9989000022140-1-1-1-1-1-14989-81811146571332780028062256227478000
999360000112361-2-2-2-2-20000000000001
100015000023230-2-2-2-2-2-2456966434202527009664342026120001